# Known Use Cases (KUC) Total: **38** ## `KUC-101` **Source**: `examples/benchmarks/LightGBM/features_resample_N.py` Resampling high-frequency 1-minute data to lower frequencies (e.g., daily) for downstream feature computation and model training. ## `KUC-102` **Source**: `examples/benchmarks/LightGBM/features_sample.py` Resampling 1-minute data to daily frequency by extracting a specific minute point from each day for feature generation. ## `KUC-103` **Source**: `examples/benchmarks/LightGBM/multi_freq_handler.py` Loading and processing data with both daily frequency features and 15-minute frequency features for models that leverage multiple time scales. ## `KUC-104` **Source**: `examples/benchmarks/TFT/tft.py` Training and evaluating a Temporal Fusion Transformer model using Alpha158 features for stock return prediction. ## `KUC-105` **Source**: `examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py` Formatting Alpha158 dataset specifically for Temporal Fusion Transformer models with proper column definitions and transformations. ## `KUC-106` **Source**: `examples/benchmarks/TFT/libs/hyperparam_opt.py` Optimizing hyperparameters for Temporal Fusion Transformer models using random search, supporting both single-GPU and distributed training. ## `KUC-107` **Source**: `examples/benchmarks/TRA/example.py` Training a Temporal Routing Attention model for stock prediction using LSTM-based architecture with configurable seed for reproducibility. ## `KUC-108` **Source**: `examples/benchmarks/TRA/src/dataset.py` Creating time series slices for the TRA model, handling multi-index pandas data with instrument and datetime levels for sequential model training. ## `KUC-109` **Source**: `examples/benchmarks/TRA/src/model.py` Implementing the Temporal Routing Attention neural network model with configurable LSTM cells and loss functions for stock prediction. ## `KUC-110` **Source**: `examples/benchmarks/TRA/Reports.ipynb` Analyzing and reporting backtest results for TRA model including MSE, MAE, IC metrics and top-N ranking performance. ## `KUC-111` **Source**: `examples/benchmarks_dynamic/DDG-DA/workflow.py` Running domain adaptation workflow for dynamic trading strategies using DDG-DA method to adapt models across different market regimes. ## `KUC-112` **Source**: `examples/benchmarks_dynamic/baseline/rolling_benchmark.py` Running rolling window benchmarks for model evaluation using fixed configurations with Linear and LightGBM models on Alpha158 features. ## `KUC-113` **Source**: `examples/data_demo/data_cache_demo.py` Demonstrating how to serialize and cache processed data handlers to disk to avoid redundant data preprocessing operations. ## `KUC-114` **Source**: `examples/data_demo/data_mem_resuse_demo.py` Demonstrating how to reuse processed data in memory across multiple training iterations to improve efficiency. ## `KUC-115` **Source**: `examples/highfreq/highfreq_handler.py` Handling high-frequency 1-minute market data with specific feature configurations for short-term trading models. ## `KUC-116` **Source**: `examples/highfreq/highfreq_ops.py` Implementing operators for high-frequency data processing including DayLast (daily last value), forward/backward fill, date extraction, and null handling. ## `KUC-117` **Source**: `examples/highfreq/highfreq_processor.py` Normalizing high-frequency price and volume data using median-based scaling for consistent feature ranges across instruments. ## `KUC-118` **Source**: `examples/highfreq/workflow.py` Executing end-to-end high-frequency trading workflow from data loading through model training to signal generation for minute-level strategies. ## `KUC-119` **Source**: `examples/hyperparameter/LightGBM/hyperparameter_158.py` Optimizing LightGBM hyperparameters using Optuna for Alpha158 feature dataset to find best model configuration for stock prediction. ## `KUC-120` **Source**: `examples/hyperparameter/LightGBM/hyperparameter_360.py` Optimizing LightGBM hyperparameters using Optuna for Alpha360 (high-frequency) feature dataset for minute-level prediction models. ## `KUC-121` **Source**: `examples/model_interpreter/feature.py` Extracting and analyzing feature importance from trained GBDT models to understand which factors drive model predictions. ## `KUC-122` **Source**: `examples/model_rolling/task_manager_rolling.py` Managing rolling training tasks across multiple time periods with task generation, collection, and multiprocessing support. ## `KUC-123` **Source**: `examples/nested_decision_execution/workflow.py` Running backtests for nested decision trading strategies that execute at multiple frequencies (daily and 30-minute) with portfolio analysis. ## `KUC-124` **Source**: `examples/online_srv/online_management_simulate.py` Simulating online model management with rolling tasks, including model updates and signal generation for live trading scenarios. ## `KUC-125` **Source**: `examples/online_srv/rolling_online_management.py` Managing online models with rolling updates including first training, routine updates, strategy addition, and signal preparation for production. ## `KUC-126` **Source**: `examples/online_srv/update_online_pred.py` Updating online model predictions with first training and subsequent prediction refreshes for production deployment. ## `KUC-127` **Source**: `examples/orderbook_data/create_dataset.py` Importing raw orderbook data (ticks, orders, transactions) into Qlib's data storage using Arctic time-series database. ## `KUC-128` **Source**: `examples/orderbook_data/example.py` Demonstrating how to use imported orderbook data in Qlib with custom providers and non-aligned time series handling. ## `KUC-129` **Source**: `examples/portfolio/prepare_riskdata.py` Preparing portfolio risk model data using Structured Covariance Estimator for risk management and portfolio optimization. ## `KUC-130` **Source**: `examples/rl/simple_example.ipynb` Implementing a simple reinforcement learning simulator and state interpreter for RL-based trading agent development. ## `KUC-131` **Source**: `examples/rl_order_execution/scripts/gen_pickle_data.py` Generating pickle-format training data for reinforcement learning order execution from high-frequency backtest results. ## `KUC-132` **Source**: `examples/rl_order_execution/scripts/gen_training_orders.py` Generating synthetic training orders for RL order execution by sampling from historical volume distributions with train/valid/test splits. ## `KUC-133` **Source**: `examples/rl_order_execution/scripts/merge_orders.py` Merging multiple order files into consolidated train/valid/test datasets for RL order execution model training. ## `KUC-134` **Source**: `examples/rolling_process_data/rolling_handler.py` Creating a data handler specifically designed for rolling window data processing with configurable time ranges. ## `KUC-135` **Source**: `examples/rolling_process_data/workflow.py` Executing rolling window workflows for model training and evaluation with pre-processor caching to optimize repeated runs. ## `KUC-136` **Source**: `examples/run_all_model.py` Running comprehensive benchmarks across multiple models with standardized configurations for fair model comparison and evaluation. ## `KUC-137` **Source**: `examples/tutorial/detailed_workflow.ipynb` Learning Qlib's detailed workflow including data access, feature computation, model training, and portfolio analysis through interactive examples. ## `KUC-138` **Source**: `examples/workflow_by_code.py` Building Qlib workflows programmatically using Python code instead of YAML configuration for fine-grained control over the pipeline.